Update README.md

README.md

@@ -32,7 +32,7 @@ Then you can use the model like this:
 from sentence_transformers import SentenceTransformer
 sentences = ["こんにちは、世界！", "文埋め込み最高！文埋め込み最高と叫びなさい", "極度乾燥しなさい"]
 
-model = SentenceTransformer("unsup-simcse-ja-large")
+model = SentenceTransformer("cl-nagoya/unsup-simcse-ja-large")
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
@@ -55,8 +55,8 @@ def cls_pooling(model_output, attention_mask):
 sentences = ['This is an example sentence', 'Each sentence is converted']
 
 # Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained("unsup-simcse-ja-large")
-model = AutoModel.from_pretrained("unsup-simcse-ja-large")
+tokenizer = AutoTokenizer.from_pretrained("cl-nagoya/unsup-simcse-ja-large")
+model = AutoModel.from_pretrained("cl-nagoya/unsup-simcse-ja-large")
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')